A novel surrogate model to support building energy labelling system: A new approach to assess cooling energy demand in commercial buildings

Melo, A.P.; Versage, R.S.; Sawaya, Geof; Lamberts, Roberto

doi:10.1016/j.enbuild.2016.09.033

Cited by 60 publications

(31 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These techniques were applied in articles focusing on almost all types of investigation within this field, as can be seen in the analysis of the key terms topic. For example, to estimate the energy performance of buildings (Melo et al 2016); to understand the influential characteristics of energy consumption in buildings (Ma and Cheng 2016); and for prediction of the energy consumption of buildings (Zhou et al 2016).…”

Section: Identifying the Data Analysis Techniques Employedmentioning

confidence: 99%

Energy efficiency in buildings: analysis of scientific literature and identification of data analysis techniques from a bibliometric study

2017

View full text Add to dashboard Cite

This study uses bibliometrics methods to analyze the specialized literature of energy efficiency in buildings, including the Scopus database during the period of time ranging from 1980 to 2016, to identify the most relevant publications, authors, researcher groups, the evolution of the theme over the years, journals, geographical areas and eventually data analysis techniques employed. The countries with the most contributions have been the USA, China and the UK, where the Lawrence Berkeley National Labor,

show abstract

Section: Identifying the Data Analysis Techniques Employedmentioning

confidence: 99%

Energy efficiency in buildings: analysis of scientific literature and identification of data analysis techniques from a bibliometric study

2017

View full text Add to dashboard Cite

show abstract

“…In the past decade, metamodels have become increasingly popular in the field of energy sources because of their significant advantages in reducing the computational cost of time-consuming tasks [1,2]. Melo et al [3] pointed out that researchers in many countries are developing metamodels to estimate the energy performance of the building stock. Bornatico et al [4] used a kind of metamodel to optimize energy systems, and found that the metamodel converged to the same solution at 150 times the speed of the fine model.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Regression-Type and Interpolation-Type Metamodels

et al. 2020

View full text Add to dashboard Cite

Metamodels have become increasingly popular in the field of energy sources because of their significant advantages in reducing the computational cost of time-consuming tasks. Lacking the prior knowledge of actual physical systems, it may be difficult to find an appropriate metamodel in advance for a new task. A favorite way of overcoming this difficulty is to construct an ensemble metamodel by assembling two or more individual metamodels. Motivated by the existing works, a novel metamodeling approach for building the ensemble metamodels is proposed in this paper. By thoroughly exploring the characteristics of regression-type and interpolation-type metamodels, some useful information is extracted from the feedback of the regression-type metamodels to further improve the functional fitting capability of the ensemble metamodels. Four types of ensemble metamodels were constructed by choosing four individual metamodels. Common benchmark problems are chosen to compare the performance of the individual and ensemble metamodels. The results show that the proposed metamodeling approach reduces the risk of selecting the worst individual metamodel and improves the accuracy of the used individual metamodels.

show abstract

“…The size of the sample is problem-dependent [16], and it can varies from 2.2× [17,18] to 4166.6×the number of design variables [19] in different BPS applications. Actually, until today, the correct size of the sample for a given building has to be determined by trial and error, which may seriously compromise the advantages of this method.…”

Section: Introductionmentioning

confidence: 99%

A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings

Bre

Fachinotti

2017

Energy and Buildings

131

View full text Add to dashboard Cite

A novel surrogate model to support building energy labelling system: A new approach to assess cooling energy demand in commercial buildings

Cited by 60 publications

References 27 publications

Energy efficiency in buildings: analysis of scientific literature and identification of data analysis techniques from a bibliometric study

Energy efficiency in buildings: analysis of scientific literature and identification of data analysis techniques from a bibliometric study

Ensemble of Regression-Type and Interpolation-Type Metamodels

A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings

Contact Info

Product

Resources

About